Assistant Machine Learning Engineer

About this role

As an Assistant Machine Learning Engineer you will report to the Trusso Technical Lead and you will join our Analytics CoE in Sofia to help maintain analytical solutions for Experian clients. You will focus on data intelligence, automation of analytical procedures, and the delivery of analytical and machine learning solutions across cloud and on-premises environments. You will combine machine learning engineering, software development, data analysis, and automation.

You will contribute to predictive model development. You will do ad-hoc analysis, write production-level code, support automated testing and deployment, and help improve analytical workflows to improve operational efficiency and speed time-to-market. You will work with data scientists, product managers, and other teams to create analytical solutions. What you'll do: Develop solutions, including data analysis, predictive model development, and ad-hoc analysis, delivering accurate and reliable production-level code to meet our requirements.

Help develop and maintain machine learning pipelines for data ingestion, transformation, and model training. Integrate communication, collaboration, and automation across the flow of work to improve processes, reduce IT operational costs, and increase speed-to-market. Conduct tests of automated solutions and support reliable delivery of analytical applications. Produce technical documentation and ensure solutions comply with Experian internal best practices and requirements.

Conduct code reviews of peers work with a high level of diligence, attention, and proficiency. Help deliver proofs of concept to evaluate analytical and technical approaches. Identify opportunities for automation and contribute to cost/benefit analyses that justify the value of proposed improvements. Work on assigned activities, gathering input from colleagues and management to address issues within the project team. Ensure a clear and full understanding of client and customer requirements and translate those needs into analytical or technical solutions.

Understand and balance the varied interests and conflicting needs of partners and communicating it. Maintain positive daily client and customer relationships while supporting successful project delivery. Develop competency and knowledge across software development and analytics, maintaining an understanding of both disciplines. Remain current on recent developments in data assets, analytical methods, and software tools through internal and external training and apply relevant learning to project work.